Sparse Multi-viewshed Analysis for Assisting Video Surveillance Network Location Optimization

In light of the challenges faced by the research on location optimization for video surveillance networks, such as the computational explosion resulting from multiple surveillance cameras performing multi-viewshed analysis, the difficulty in targeting specific regions of interest, and the tendency for optimization solutions to become trapped in local searches, this study aims to address these issues. This paper presents a sparse multi-viewshed analysis algorithm and a model for optimizing the location selection of video surveillance networks. Experimental investigations are conducted in various scenarios to validate the proposed approach for video surveillance network location optimization. The results demonstrate that the sparse multi-viewshed analysis algorithm significantly enhances the efficiency of multi-viewshed analysis. The optimizing approach yielded high-quality coverage solutions for both region-specific and comprehensive solution. The outcomes of this research contribute as a decision-making reference for efficient simulation of video surveillance network location optimization in complex geographical contexts.